fix: ollama openai completion and chat completion params (#2125)

# What does this PR do?

The ollama provider was using an older variant of the code to convert
incoming parameters from the OpenAI API completions and chat completion
endpoints into requests that get sent to the backend provider over its
own OpenAI client. This updates it to use the common
`prepare_openai_completion_params` method used elsewhere, which takes
care of removing stray `None` values even for nested structures.

Without this, some other parameters, even if they have values of `None`,
make their way to ollama and actually influence its inference output as
opposed to when those parameters are not sent at all.

## Test Plan

This passes tests/integration/inference/test_openai_completion.py and
fixes the issue found in #2098, which was tested via manual curl
requests crafted a particular way.

Closes #2098

Signed-off-by: Ben Browning <bbrownin@redhat.com>

llama_stack/providers/remote/inference/ollama/ollama.py

@@ -61,6 +61,7 @@ from llama_stack.providers.utils.inference.openai_compat import (
     OpenAICompatCompletionChoice,
     OpenAICompatCompletionResponse,
     get_sampling_options,
+    prepare_openai_completion_params,
     process_chat_completion_response,
     process_chat_completion_stream_response,
     process_completion_response,
@@ -395,29 +396,25 @@ class OllamaInferenceAdapter(
             raise ValueError("Ollama does not support non-string prompts for completion")
 
         model_obj = await self._get_model(model)
-        params = {
-            k: v
-            for k, v in {
-                "model": model_obj.provider_resource_id,
-                "prompt": prompt,
-                "best_of": best_of,
-                "echo": echo,
-                "frequency_penalty": frequency_penalty,
-                "logit_bias": logit_bias,
-                "logprobs": logprobs,
-                "max_tokens": max_tokens,
-                "n": n,
-                "presence_penalty": presence_penalty,
-                "seed": seed,
-                "stop": stop,
-                "stream": stream,
-                "stream_options": stream_options,
-                "temperature": temperature,
-                "top_p": top_p,
-                "user": user,
-            }.items()
-            if v is not None
-        }
+        params = await prepare_openai_completion_params(
+            model=model_obj.provider_resource_id,
+            prompt=prompt,
+            best_of=best_of,
+            echo=echo,
+            frequency_penalty=frequency_penalty,
+            logit_bias=logit_bias,
+            logprobs=logprobs,
+            max_tokens=max_tokens,
+            n=n,
+            presence_penalty=presence_penalty,
+            seed=seed,
+            stop=stop,
+            stream=stream,
+            stream_options=stream_options,
+            temperature=temperature,
+            top_p=top_p,
+            user=user,
+        )
         return await self.openai_client.completions.create(**params)  # type: ignore
 
     async def openai_chat_completion(
@@ -447,35 +444,31 @@ class OllamaInferenceAdapter(
         user: str | None = None,
     ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
         model_obj = await self._get_model(model)
-        params = {
-            k: v
-            for k, v in {
-                "model": model_obj.provider_resource_id,
-                "messages": messages,
-                "frequency_penalty": frequency_penalty,
-                "function_call": function_call,
-                "functions": functions,
-                "logit_bias": logit_bias,
-                "logprobs": logprobs,
-                "max_completion_tokens": max_completion_tokens,
-                "max_tokens": max_tokens,
-                "n": n,
-                "parallel_tool_calls": parallel_tool_calls,
-                "presence_penalty": presence_penalty,
-                "response_format": response_format,
-                "seed": seed,
-                "stop": stop,
-                "stream": stream,
-                "stream_options": stream_options,
-                "temperature": temperature,
-                "tool_choice": tool_choice,
-                "tools": tools,
-                "top_logprobs": top_logprobs,
-                "top_p": top_p,
-                "user": user,
-            }.items()
-            if v is not None
-        }
+        params = await prepare_openai_completion_params(
+            model=model_obj.provider_resource_id,
+            messages=messages,
+            frequency_penalty=frequency_penalty,
+            function_call=function_call,
+            functions=functions,
+            logit_bias=logit_bias,
+            logprobs=logprobs,
+            max_completion_tokens=max_completion_tokens,
+            max_tokens=max_tokens,
+            n=n,
+            parallel_tool_calls=parallel_tool_calls,
+            presence_penalty=presence_penalty,
+            response_format=response_format,
+            seed=seed,
+            stop=stop,
+            stream=stream,
+            stream_options=stream_options,
+            temperature=temperature,
+            tool_choice=tool_choice,
+            tools=tools,
+            top_logprobs=top_logprobs,
+            top_p=top_p,
+            user=user,
+        )
         return await self.openai_client.chat.completions.create(**params)  # type: ignore
 
     async def batch_completion(